Background: An effective learning healthcare system needs measures that help managers identify how to promote system-level improvements. One opportunity to influence system level improvements is to directly measure the care sequences provided to patients that may reflect decreased efficiency and increased care fragmentation. We propose to determine whether a VA administrative data can be used to construct reliable measures of care sequences. Significance/Impact: Our innovation is in adapting a data science sequence analysis methodology to VA administrative records. This methodology has the potential to highlight care fragmentation and integration. Fragmentation is arguably the most important underemphasized goal in VA. Performance goals exist for quality of care and access, and there is a strong infrastructure for managing cost. However, there is limited focus on reducing fragmentation and improving integration, in part due to the lack of adequate measures. VA priorities include more efficient resource use. Two VHA strategies to increase efficiency are to diligently find areas of waste and correct to generate savings, and to improve the delivery of health care services by ensuring care coordination across all care settings. Sequence-based fragmentation and integration measures have the potential to directly inform these strategies. Specific Aim: The specific aim of this two-year proposal is to determine whether VA administrative data can be used to reliably measure mental health sequences of care. As an exploratory aim, we will determine whether sequences may represent care fragmentation. Methodology: We will use the VA Corporate Data Warehouse to collect evidence for internal consistency and test-retest reliability. Approximately 46,000 Veterans will be sampled in each of 6 annual cohorts (FY2013-FY2018) across 54 medical centers. We will use sequence analysis to identify clusters of similar sequences that are characterized by a common consensus sequential pattern. Internal consistency will be determined by comparing random patient samples to determine if patient sequences are more similar within consensus sequential patterns than between patterns. Test-retest reliability will compare patterns over time. We expect a step function where sequences will be similar over time, with periodic changes as capabilities improve. Generally, proximal sequences will be more similar than distal sequences. For the exploratory aim, we will calculate patient-level correlations with administrative database measures of care fragmentation and facility-level correlations with VA performance measures. A Delphi process with an expert panel will the degree to which each sequence generated by the methodology may measure fragmentation. This will provide preliminary data for the next study. Next Steps/Implementation: This two-year study will determine whether the sequence analysis method can be applied to VA administrative data to identify reliable care sequences. The output of the Delphi process and the convergent and discriminant validity tests will allow the team to develop specific hypotheses about VA depression care sequences that will be tested in a follow- up study. The next step will be to determine the association of depression care sequences with mental health symptoms, functioning, satisfaction, and cost. Our long-term goal is to develop a method for measuring care sequences in near-real time and provide feedback to managers and clinicians to identify patients regarding care sequences that may require intervention.